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In public, smoking behavior not only causes pathological harm to human health, but also exerts danger of fire hazards, etc. For the health and safety considerations, this paper designs a real-time smoking detection model based on deep learning for airports, gas stations, chemical warehouses and other places where smoking is strictly prohibited. This model uses a convolutional neural network to process the input frame from the video stream captured by the webcam. Through the process of image feature extraction, feature fusion, target classification and target posi-tioning, the coordinate of the cigarette is located, and then the smoking behaviors can be found out. Common object detection algorithms are not ideal for small target objects and the detection speed needs to be improved. This paper designs a series of convolutional neural network modules to reduce the amount of model parameters and pick up the inference speed to meet real-time requirements as well as improving the accuracy of small target object (cigarette) detection. This paper also comes up with some training skills to make the model more robust. Due to the lack of relevant dataset, this paper produces a dataset related to smoking behaviors. Through comparative experiments, it is proven that the algorithm proposed in this paper has better detection effects on proposed dataset and some public datasets.

In the process of unconventional oil and gas reservoir exploitation, it is difficult to reduce drilling fluid lost in natural fractures, enhance the CO2 displacement effect and reduce foam drainage gas recovery costs. In most cases, foaming agents can solve these problems in a low-cost way in a short period of time. Foaming agent screening and evaluation is the key to this technology. However, there are few experimental tests used in the evaluation of foaming agent properties that match the actual unconventional oil or gas well conditions of high temperature and high pressure. Using the actual temperature and pressure conditions of a wellbore, the foaming capacity and half-life of two common foaming agents were systematically evaluated by using the high-temperature and high-pressure visual foam properties evaluation device (UPMX-500), in which the foaming agent's volume concentration was 3‰ in a simulated formation water with a pH of 6 and salinity of 9 × 104 mg/L. The high-temperature (40 °C, 60 °C, 80 °C, 100 °C) and high-pressure (0.1 MPa, 6.0 MPa, 8.0 MPa, 10.0 MPa) effect on the foaming capacity and half-life was analyzed. Binary linear regression of pressure and temperature was carried out, taking the foam composite index as the target and using a formula with high correlation. The results showed that the foam composite index (FCI) of the two foaming agents was positively correlated with pressure and temperature. The correlation of UT-7 was FCI = 64.1196T + 735.713p − 2066.2, the correlation of HY-3K was FCI = 62.5523T + 7220.391p − 2415.6, and the coefficients of determination were 0.9799 and 0.9895, respectively, with an error of less than 10%. This correlation equation can provide a reference for accurately predicting the foaming capacity of foaming agents under high-temperature and high-pressure conditions and can also be used to optimize foaming agents or to qualitatively evaluate results for the efficient exploitation of unconventional oil and gas reservoirs. [ABSTRACT FROM AUTHOR]

Cables are critical in engineering structures for load‐bearing, electronic connection, and mechanical transmission. Various cable‐crawling robots (CCRs) have been developed to perform scheduled inspection or convey supplies. Most existing CCRs are often actuated by motors and used in large‐scaled engineering structures. The heavy bodies of these CCRs can cause damage or even casualties once slippage or drop occurs. A small and lightweight CCR that can crawl on thin cables is highly demanded for safety inspection in narrow and confined inner spaces of engineering structures. Herein, a soft CCR (weight, 2.1 g; length, 43 mm) is developed by utilizing multilayered dielectric elastomer actuators. Compared with existing solutions, this CCR achieves crawling on thin cables (diameter: <1 mm) while crawling fastest (horizontal: 0.72 body length per second). The CCR is also capable of transporting objects (horizontal: 3.69 times its own weight; vertical: 0.76 times its own weight), climbing upward on a vertical cable, and locomoting across the water–air interface. The CCR is also demonstrated to crawl on a slack cable and circular/spiral cables. Finally, the soft robot, equipped with an endoscope, demonstrates inspections on a tensegrity structure as well as in an airplane wing model with a preplaced cable. [ABSTRACT FROM AUTHOR]

Regulatory T cells (Tregs) are a subset of T cells participating in a variety of diseases including mycoplasmal pneumonia, contagious ecthyma, and so on. The role of Tregs in goat contagious ecthyma is not completely understood due to the lack of species-specific antibodies. Here, we developed a combination of CD4 and CD25 fluorescence monoclonal antibodies (mAb) to recognize goat Tregs and assessed its utility in flow cytometry, immunofluorescence staining. Using immunofluorescence staining, we found that the frequency of Treg cells was positively correlated with the viral load during orf virus infection. These antibodies could serve as important tools to monitor Tregs during orf virus infection in goats. Key points: • A combination of fluorescent mAbs (C11 and D12) was prepared for the detection of goat Tregs. • C11 and D12 are effective in flow cytometry, immunofluorescence staining, and C11 has excellent species specificity. • The frequency of Treg cells was positively correlated with the viral load during orf virus infection. [ABSTRACT FROM AUTHOR]

Free radical polymerization was selected to obtain a promising copolymer scale inhibitor IA‐AM‐SAS with good water solubility and temperature and salt resistance, which is suitable for the treatment of surface pipeline water in oil fields, using itaconic acid (IA), acrylamide (AM), and sodium acrylsulfonate (SAS) as the monomers. The structure of the synthesized copolymer was verified by Fourier transform infrared spectroscopy and nuclear magnetic resonance hydrogen spectroscopy. Thermogravimetric results showed that the copolymer does not undergo significant thermal degradation at temperatures below 356°C, indicating that the copolymer has good thermal stability. The molecular weights of the polymers at different monomer ratios were measured using gel permeation chromatography and the relationship between the molecular weights and scale inhibition performance was discussed. The results showed that the scale inhibition effect was best when the monomer molar ratio n(IA): n(AM): n(SAS) was 0.5:2:1.5 and the number of average molecular weight of the prepared copolymer was 7712 g/mol, and the inhibition efficiency of CaCO3 was 84.02% when IA‐AM‐SAS was at a concentration of 30 mg L−1, and the inhibition rate was measured by the standard of static scale inhibition test in the oilfield measured. The range of conditions (PH, Ca2+ concentration, water temperature, and time) of the static scale inhibition test was then expanded using a one‐way controlled variable experiment to explore the performance of IA‐AM‐SAS scale inhibitors under different water quality conditions. The scale inhibition mechanism was explored by scanning electron microscopy, x‐ray diffraction, and x‐ray photoelectron spectroscopy. Briefly, the combination of multiple functional groups enables IA‐AM‐SAS to be applied in complex and challenging environments. [ABSTRACT FROM AUTHOR]